Will AI fail completely, or will it actually be useful for building software going forward? I took GitHub Spec Kit for a spinn when building this blog, and the results were awesome. I have had limited experience with both Hugo and GitHub Pages before beginning, so I figured that this would be a good test case.

What is GitHub Spec Kit?

GitHub Spec Kit is a GitHub-powered workflow that emphasizes specification-first development. You define what you’re building in structured markdown documents before writing a single line of code.

Spec-Driven Development flips the script on traditional software development. For decades, code has been king — specifications were just scaffolding we built and discarded once the “real work” of coding began. Spec-Driven Development changes this: specifications become executable, directly generating working implementations rather than just guiding them.

The Workflow

GitHub Spec Kit uses slash-commands to make sure that things are done right from the beginning. It starts with defining non-negotiable principles, then moves to specification, planning, task breakdown, and finally implementation. It’s like the waterfall model introduced in the 1970’s, but with modern agility and AI assistance.

Before we can use these commands, we need to enable the project to use them. All that stuff is described here.

1. /speckit.constitution – Establish Project Principles First

You begin by defining non‑negotiable governance rules (semantic HTML, no JS/CSS frameworks, performance, progressive enhancement, content focus). These principles:

  • Gate later architectural choices
  • Feed “Constitution Check” blocks in later templates
  • Prevent speculative tech decisions from leaking into early specs

Without this, later artifacts drift or require rework when principles are retrofitted.

2. /speckit.specify → /speckit.clarify – Define & De‑risk the Feature

/speckit.specify turns a natural language idea into spec.md (user stories, success criteria, assumptions). It also applies a quality checklist (governance alignment, testability, scope boundaries).

Then /speckit.clarify (optional but recommended) performs a 10‑dimension ambiguity scan (scope, data, UX, errors, performance, security, etc.) and asks up to 5 high‑impact questions. Answers are inlined into the spec immediately and logged under a Clarifications section.

Result: A specification that is testable, governance‑aligned, and low‑risk to plan against.

3. /speckit.plan – Technical Design & Research

Generates the implementation plan while honoring the constitution and the clarified spec:

This is where Hugo, content sectioning, system fonts, and no-build CSS strategy were formalized.

4. /speckit.tasks → /speckit.analyze – Breakdown & Coverage Validation

/speckit.tasks converts design + spec into an ordered task graph (64 tasks here) with phases (Setup → Foundational → User Stories → Polish). Each task is atomic (single, verifiable outcome).

Then /speckit.analyze validates that everything still makes sense. This step catches drift early (e.g., a task implementing something never specified).

5. /speckit.implement – Guided Execution

Automates phased execution:

  1. Verifies checklists complete
  2. Ensures repo hygiene (.gitignore, etc.)
  3. Executes sequential vs [P] parallel tasks safely
  4. Marks progress back into tasks.md
  5. Stops on governance or unmet dependency violations

6. Fixing Issues & Iteration

It’s still an AI and LLM that does tings, so mistakes happen. But with the spec, plan, and tasks as ground truth, debugging is faster. Each issue traces back to a documented decision or requirement. After a few iterations, using both promopts and manual minor fixes, the site was ready in less than a day.

Why It Worked

Clarity Before Code: Writing specifications forced me to think through edge cases and design patterns before implementation. No more “figure it out as you go” chaos.

Constitutional Principles: I established a constitution.md (v1.2.0) with core principles—simplicity, semantic HTML, progressive enhancement, performance, and content‑centric design. Every feature had to pass constitutional review.

AI-Assisted Implementation: With detailed specs, GitHub Copilot understood exactly what I was building. The implementation phase moved fast because the planning phase was thorough.

Built-in Documentation: My specs became my documentation. When I added dark/light themes or Mermaid diagram support, I updated the specs—no separate docs to maintain.

Would I Do It Again?

Absolutely. The slash command workflow adds structure without to much bureaucracy. The GitHub Spec Kit is most definitiely in early stages, but it shows great promise for improving software development processes. By focusing on clear specifications and governance from the start, I was able to build this blog efficiently and with confidence. If you’re looking to streamline your development workflow and reduce rework, I highly recommend giving GitHub Spec Kit a try.